{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 面积图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.collections.PolyCollection at 0x21419433a30>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [1, 2, 3, 4, 5]\n",
    "y = np.random.randint(10, 100, 5)\n",
    "\n",
    "plt.stackplot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pandas获取Excel数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年份</th>\n",
       "      <th>销售额</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>1962035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>2838693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>2317447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>2335002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>2438570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019</td>\n",
       "      <td>1675591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020</td>\n",
       "      <td>3568120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     年份      销售额\n",
       "0  2014  1962035\n",
       "1  2015  2838693\n",
       "2  2016  2317447\n",
       "3  2017  2335002\n",
       "4  2018  2438570\n",
       "5  2019  1675591\n",
       "6  2020  3568120"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('data/plot.xlsx', sheet_name='stackplot1')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = df.年份, df.销售额"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.stackplot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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